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A discrete bio-inspired metaheuristic algorithm for efficient and accurate image matting

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Abstract

With the development of digital multimedia technologies, image matting has become one of the most popular research problem in academic field and been widely applied in industrial communities. The key challenge of image matting is how to extract the foreground region (target region) from a given image accurately. Sampling-based image matting technology implements matting by sampling some foreground pixels and background pixels from known regions and finding the best foreground–background sample pair for every undetermined pixel. The best foreground–background sample pair represents the true foreground and background colors of the corresponding undetermined pixel and they can estimate the region of this undetermined pixel accurately. Therefore, the quality of matting depends on whether the best sample pair can be found. This search process can be regarded as a combinational optimization problem. In this paper, in order to obtain more accurate matting result, we applied a bio-inspired metaheuristic algorithm to solve this problem, which is based on the promising earthworm optimization algorithm (EWA). By analyzing the property of this optimization problem, we upgrade two reproductions and the cauchy mutation of EWA to discrete calculations. The proposed algorithm is called as the discrete earthworm optimization algorithm (D-EWA). By comparing with existing optimization algorithms on a standard benchmark dataset, the experimental results show that the proposed D-EWA can obtain more accurate matting results on both visual effect and quantitative metric.

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Funding

This study was funded by National Natural Science Foundation of China (61370102, 61170193, 61370185), Guangdong Natural Science Foundation (2014A030306050, S2012010009865, S20130100 13432, S2013010015940), the Fundamental Research Funds for the Central Universities, SCUT (2015PT022), Science and Technology Planning Project of Huizhou City (2011P002, 2011g012, 2011P005, 2011P003, 2011g011, 2013B020015008) and Science and Technology Planning Project of Guangdong Province (2011B090400041, 2012B010100039, 2012 B040305011, 2012B010100040, 2015B010129015). Education and Science Programs of Guangdong Province (11JXZ012, 14JXN065), Discipline Construction Programs of Guangdong Province (2013LYM00874), Key Technology Research and Development Programs of Huizhou (2013-13, 2013B020015008, 2014B 050013016), Science and Technology Plan Project of Huizhou University (2012QN09), Distinguished Young Scholars Fund of Department of Education (No. Yq2013126).

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Authors and Affiliations

  1. College of Huizhou, Huizhou, 516007, China

    Zhao-Quan Cai

  2. School of Software Engineering, South China University of Technology, Guangzhou, 510006, China

    Liang Lv, Han Huang & Yi-Hui Liang

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  1. Zhao-Quan Cai
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  2. Liang Lv
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  3. Han Huang
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  4. Yi-Hui Liang
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Correspondence to Liang Lv.

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Cai, ZQ., Lv, L., Huang, H. et al. A discrete bio-inspired metaheuristic algorithm for efficient and accurate image matting. Memetic Comp. 11, 53–64 (2019). https://doi.org/10.1007/s12293-018-0275-4

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